A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Abstract

The three-dimensional (3D) reconstruction of pulmonary nodules using medical images has introduced new technical approaches for diagnosing and treating pulmonary nodules, and these approaches are progressively being acknowledged and adopted by physicians and patients. Nonetheless, constructing a relatively universal 3D digital model of pulmonary nodules for diagnosis and treatment is challenging due to device differences, shooting times, and nodule types. The objective of this study is to propose a new 3D digital model of pulmonary nodules that serves as a bridge between physicians and patients and is also a cutting-edge tool for pre-diagnosis and prognostic evaluation. Many AI-driven pulmonary nodule detection and recognition methods employ deep learning techniques to capture the radiological features of pulmonary nodules, and these methods can achieve a good area under-the-curve (AUC) performance. However, false positives and false negatives remain a challenge for radiologists and clinicians. The interpretation and expression of features from the perspective of pulmonary nodule classification and examination are still unsatisfactory. In this study, a method of continuous 3D reconstruction of the whole lung in horizontal and coronal positions is proposed by combining existing medical image processing technologies. Compared with other applicable methods, this method allows users to rapidly locate pulmonary nodules and identify their fundamental properties while also observing pulmonary nodules from multiple perspectives, thereby providing a more effective clinical tool for diagnosing and treating pulmonary nodules.